5. Every picture tells a story
Goal of computer vision is to write computer programs
that can interpret images
6. Can computers match (or beat) human vision?
Yes and no (but mostly no!)
• humans are much better at “hard” things
• computers can be better at “easy” things
12. Optical character recognition (OCR)
Digit recognition, AT&T labs
http://www.research.att.com/~yann/
Technology to convert scanned docs to text
• If you have a scanner, it probably came with OCR software
License plate readers
http://en.wikipedia.org/wiki/Automatic_number_plate_recognition
15. Object recognition (in supermarkets)
LaneHawk by EvolutionRobotics
“A smart camera is flush-mounted in the checkout lane, continuously watching
for items. When an item is detected and recognized, the cashier verifies the
quantity of items that were found under the basket, and continues to close the
transaction. The item can remain under the basket, and with LaneHawk,you are
assured to get paid for it… “
18. Login without a password…
Fingerprint scanners on
many new laptops,
other devices
Face recognition systems now
beginning to appear more widely
http://www.sensiblevision.com/
19. Object recognition (in mobile phones)
This is becoming real:
• Microsoft Research
• Point & Find, Nokia
20. The Matrix movies, ESC Entertainment, XYZRGB, NRC
Special effects: shape capture
21. Pirates of the Carribean, Industrial Light and Magic
Click here for interactive demo
Special effects: motion capture
23. Smart cars
Mobileye
• Vision systems currently in high-end BMW, GM, Volvo models
• By 2010: 70% of car manufacturers.
• Video demo
Slide content courtesy of Amnon Shashua
24. Vision-based interaction (and games)
Nintendo Wii has camera-based IR
tracking built in. See Lee’s work at
CMU on clever tricks on using it to
create a multi-touch display!
Digimask: put your face on a 3D avatar.
“Game turns moviegoers into Human Joysticks”, CNET
Camera tracking a crowd, based on this work.
25. Vision in space
Vision systems (JPL) used for several tasks
• Panorama stitching
• 3D terrain modeling
• Obstacle detection, position tracking
• For more, read “Computer Vision on Mars” by Matthies et al.
NASA'S Mars Exploration Rover Spirit captured this westward view from atop
a low plateau where Spirit spent the closing months of 2007.
28. Current state of the art
You just saw examples of current systems.
• Many of these are less than 5 years old
This is a very active research area, and rapidly changing
• Many new apps in the next 5 years
To learn more about vision applications and companies
• David Lowe maintains an excellent overview of vision
companies
– http://www.cs.ubc.ca/spider/lowe/vision.html
35. General Comments
Prerequisites—these are essential!
• Data structures
• A good working knowledge of C and C++ programming
– (or willingness/time to pick it up quickly!)
• Linear algebra
• Vector calculus
Course does not assume prior imaging experience
• computer vision, image processing, graphics, etc.